用单张图片生成可编辑的3D CAD模型，打通AI与设计软件的壁垒。
Img2CAD: Conditioned 3D CAD Model Generation from Single Image with Structured Visual Geometry
- 提出结构化视觉几何（SVG）中间表示，用矢量线框提升建模精度。
- 构建20万+规模的ABC-mono数据集和首个真实物体配对CAD的KOCAD数据集。
- 适合工业设计、智能制造领域研究人员快速生成可修改3D模型。
本文提出Img2CAD，是首个已知利用2D图像输入生成可编辑参数三维CAD模型的方法。与现有基于文本或图像生成3D模型通常依赖网格表示不同，这些方法不兼容CAD工具且缺乏可编辑性与精细控制，而Img2CAD实现了AI重建与CAD软件的无缝集成。我们创新性地引入一种称为结构化视觉几何（Structured Visual Geometry, SVG）的中间表示，其特征为从物体中提取的矢量化线框，显著提升了条件化CAD模型生成效果。此外，我们构建了两个新数据集以支持该领域研究：包含超过20万份3D CAD模型及其渲染图像的ABC-mono数据集，以及首个包含真实世界拍摄物体及其真实CAD模型的KOCAD数据集，推动条件化CAD模型生成研究发展。
原文摘要 · Abstract (English)
In this paper, we propose Img2CAD, the first approach to our knowledge that uses 2D image inputs to generate CAD models with editable parameters. Unlike existing AI methods for 3D model generation using text or image inputs often rely on mesh-based representations, which are incompatible with CAD tools and lack editability and fine control, Img2CAD enables seamless integration between AI-based 3D reconstruction and CAD software. We have identified an innovative intermediate representation called Structured Visual Geometry (SVG), characterized by vectorized wireframes extracted from objects. This representation significantly enhances the performance of generating conditioned CAD models. Additionally, we introduce two new datasets to further support research in this area: ABC-mono, the largest known dataset comprising over 200,000 3D CAD models with rendered images, and KOCAD, the first dataset featuring real-world captured objects alongside their ground truth CAD models, supporting further research in conditioned CAD model generation.
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